Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations891
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory247.6 KiB
Average record size in memory284.5 B

Variable types

Numeric7
Categorical6
Text3

Alerts

Age is highly overall correlated with Age_BinMean and 2 other fieldsHigh correlation
Age_BinMean is highly overall correlated with Age and 2 other fieldsHigh correlation
Age_Group is highly overall correlated with Age and 2 other fieldsHigh correlation
Age_Standard is highly overall correlated with Age and 2 other fieldsHigh correlation
Fare is highly overall correlated with Fare_MinMaxHigh correlation
Fare_MinMax is highly overall correlated with FareHigh correlation
Sex is highly overall correlated with SurvivedHigh correlation
Survived is highly overall correlated with SexHigh correlation
PassengerId is uniformly distributed Uniform
PassengerId has unique values Unique
Name has unique values Unique
SibSp has 608 (68.2%) zeros Zeros
Parch has 678 (76.1%) zeros Zeros
Fare has 15 (1.7%) zeros Zeros
Fare_MinMax has 15 (1.7%) zeros Zeros
Age_Standard has 177 (19.9%) zeros Zeros

Reproduction

Analysis started2025-08-21 05:38:27.022057
Analysis finished2025-08-21 05:38:29.611717
Duration2.59 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-08-21T11:08:29.650126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2025-08-21T11:08:29.703933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
599 1
 
0.1%
588 1
 
0.1%
589 1
 
0.1%
590 1
 
0.1%
591 1
 
0.1%
592 1
 
0.1%
593 1
 
0.1%
594 1
 
0.1%
595 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
0
549 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Length

2025-08-21T11:08:29.750887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-21T11:08:29.794657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring characters

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Pclass
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2025-08-21T11:08:29.834756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-21T11:08:29.873675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Name
Text

Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size66.2 KiB
2025-08-21T11:08:29.978320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr 521
 
14.4%
miss 182
 
5.0%
mrs 129
 
3.6%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
george 24
 
0.7%
james 24
 
0.7%
charles 23
 
0.6%
Other values (1515) 2538
70.0%
2025-08-21T11:08:30.165888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
1
577 
0
314 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 577
64.8%
0 314
35.2%

Length

2025-08-21T11:08:30.231932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-21T11:08:30.269049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 577
64.8%
0 314
35.2%

Most occurring characters

ValueCountFrequency (%)
1 577
64.8%
0 314
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 577
64.8%
0 314
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 577
64.8%
0 314
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 577
64.8%
0 314
35.2%

Age
Real number (ℝ)

High correlation 

Distinct89
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.699118
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-08-21T11:08:30.315026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile6
Q122
median29.699118
Q335
95-th percentile54
Maximum80
Range79.58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.002015
Coefficient of variation (CV)0.4377913
Kurtosis0.9662793
Mean29.699118
Median Absolute Deviation (MAD)6.3008824
Skewness0.43448809
Sum26461.914
Variance169.0524
MonotonicityNot monotonic
2025-08-21T11:08:30.370557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.69911765 177
 
19.9%
24 30
 
3.4%
22 27
 
3.0%
18 26
 
2.9%
28 25
 
2.8%
30 25
 
2.8%
19 25
 
2.8%
21 24
 
2.7%
25 23
 
2.6%
36 22
 
2.5%
Other values (79) 487
54.7%
ValueCountFrequency (%)
0.42 1
 
0.1%
0.67 1
 
0.1%
0.75 2
 
0.2%
0.83 2
 
0.2%
0.92 1
 
0.1%
1 7
0.8%
2 10
1.1%
3 6
0.7%
4 10
1.1%
5 4
 
0.4%
ValueCountFrequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 2
0.2%
70.5 1
 
0.1%
70 2
0.2%
66 1
 
0.1%
65 3
0.3%
64 2
0.2%
63 2
0.2%
62 4
0.4%

SibSp
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-08-21T11:08:30.415501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2025-08-21T11:08:30.455852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
ValueCountFrequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-08-21T11:08:30.494702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2025-08-21T11:08:30.533663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Ticket
Text

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size48.6 KiB
2025-08-21T11:08:30.704323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
ston/o 12
 
1.1%
2 12
 
1.1%
sc/paris 9
 
0.8%
w./c 9
 
0.8%
soton/o.q 8
 
0.7%
347082 7
 
0.6%
Other values (709) 955
84.5%
2025-08-21T11:08:30.882461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.204208
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-08-21T11:08:30.955920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.9104
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.0896

Descriptive statistics

Standard deviation49.693429
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean32.204208
Median Absolute Deviation (MAD)6.9042
Skewness4.7873165
Sum28693.949
Variance2469.4368
MonotonicityNot monotonic
2025-08-21T11:08:31.011527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
0 15
 
1.7%
Other values (238) 615
69.0%
ValueCountFrequency (%)
0 15
1.7%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.2%
6.75 2
 
0.2%
6.8583 1
 
0.1%
6.95 1
 
0.1%
ValueCountFrequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Cabin
Text

Distinct148
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size48.2 KiB
2025-08-21T11:08:31.123445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length15
Median length7
Mean length6.2188552
Min length1

Characters and Unicode

Total characters5541
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)11.3%

Sample

1st rowUnknown
2nd rowC85
3rd rowUnknown
4th rowC123
5th rowUnknown
ValueCountFrequency (%)
unknown 687
74.3%
c25 4
 
0.4%
c27 4
 
0.4%
g6 4
 
0.4%
b96 4
 
0.4%
b98 4
 
0.4%
f 4
 
0.4%
c23 4
 
0.4%
f33 3
 
0.3%
e101 3
 
0.3%
Other values (152) 204
 
22.1%
2025-08-21T11:08:31.305334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2061
37.2%
U 687
 
12.4%
k 687
 
12.4%
o 687
 
12.4%
w 687
 
12.4%
2 72
 
1.3%
C 71
 
1.3%
B 64
 
1.2%
1 61
 
1.1%
3 59
 
1.1%
Other values (14) 405
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5541
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2061
37.2%
U 687
 
12.4%
k 687
 
12.4%
o 687
 
12.4%
w 687
 
12.4%
2 72
 
1.3%
C 71
 
1.3%
B 64
 
1.2%
1 61
 
1.1%
3 59
 
1.1%
Other values (14) 405
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5541
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2061
37.2%
U 687
 
12.4%
k 687
 
12.4%
o 687
 
12.4%
w 687
 
12.4%
2 72
 
1.3%
C 71
 
1.3%
B 64
 
1.2%
1 61
 
1.1%
3 59
 
1.1%
Other values (14) 405
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5541
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2061
37.2%
U 687
 
12.4%
k 687
 
12.4%
o 687
 
12.4%
w 687
 
12.4%
2 72
 
1.3%
C 71
 
1.3%
B 64
 
1.2%
1 61
 
1.1%
3 59
 
1.1%
Other values (14) 405
 
7.3%

Embarked
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size43.6 KiB
2
644 
0
168 
1
77 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 644
72.3%
0 168
 
18.9%
1 77
 
8.6%
3 2
 
0.2%

Length

2025-08-21T11:08:31.374644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-21T11:08:31.414370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 644
72.3%
0 168
 
18.9%
1 77
 
8.6%
3 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 644
72.3%
0 168
 
18.9%
1 77
 
8.6%
3 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 644
72.3%
0 168
 
18.9%
1 77
 
8.6%
3 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 644
72.3%
0 168
 
18.9%
1 77
 
8.6%
3 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 644
72.3%
0 168
 
18.9%
1 77
 
8.6%
3 2
 
0.2%

Age_BinMean
Categorical

High correlation 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size57.0 KiB
26.256680350916653
523 
39.23936170212766
188 
8.0067
100 
54.84782608695652
69 
69.77272727272727
 
11

Length

Max length18
Median length18
Mean length16.352413
Min length6

Characters and Unicode

Total characters14570
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26.256680350916653
2nd row39.23936170212766
3rd row26.256680350916653
4th row39.23936170212766
5th row39.23936170212766

Common Values

ValueCountFrequency (%)
26.256680350916653 523
58.7%
39.23936170212766 188
 
21.1%
8.0067 100
 
11.2%
54.84782608695652 69
 
7.7%
69.77272727272727 11
 
1.2%

Length

2025-08-21T11:08:31.460452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-21T11:08:31.504245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
26.256680350916653 523
58.7%
39.23936170212766 188
 
21.1%
8.0067 100
 
11.2%
54.84782608695652 69
 
7.7%
69.77272727272727 11
 
1.2%

Most occurring characters

ValueCountFrequency (%)
6 3497
24.0%
2 1814
12.5%
5 1776
12.2%
3 1610
11.1%
0 1503
10.3%
9 979
 
6.7%
1 899
 
6.2%
. 891
 
6.1%
8 830
 
5.7%
7 633
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 3497
24.0%
2 1814
12.5%
5 1776
12.2%
3 1610
11.1%
0 1503
10.3%
9 979
 
6.7%
1 899
 
6.2%
. 891
 
6.1%
8 830
 
5.7%
7 633
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 3497
24.0%
2 1814
12.5%
5 1776
12.2%
3 1610
11.1%
0 1503
10.3%
9 979
 
6.7%
1 899
 
6.2%
. 891
 
6.1%
8 830
 
5.7%
7 633
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 3497
24.0%
2 1814
12.5%
5 1776
12.2%
3 1610
11.1%
0 1503
10.3%
9 979
 
6.7%
1 899
 
6.2%
. 891
 
6.1%
8 830
 
5.7%
7 633
 
4.3%

Fare_MinMax
Real number (ℝ)

High correlation  Zeros 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062858428
Minimum0
Maximum1
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-08-21T11:08:31.558853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.014102261
Q10.015440073
median0.028212719
Q30.06050797
95-th percentile0.21876393
Maximum1
Range1
Interquartile range (IQR)0.045067898

Descriptive statistics

Standard deviation0.096995113
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean0.062858428
Median Absolute Deviation (MAD)0.013476101
Skewness4.7873165
Sum56.006859
Variance0.0094080519
MonotonicityNot monotonic
2025-08-21T11:08:31.614776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01571255357 43
 
4.8%
0.02537431011 42
 
4.7%
0.01541157521 38
 
4.3%
0.01512699257 34
 
3.8%
0.05074862022 31
 
3.5%
0.02049463509 24
 
2.7%
0.01546856982 18
 
2.0%
0.01517578932 16
 
1.8%
0.01411045867 15
 
1.7%
0 15
 
1.7%
Other values (238) 615
69.0%
ValueCountFrequency (%)
0 15
1.7%
0.007831878409 1
 
0.1%
0.009759350043 1
 
0.1%
0.01217478918 1
 
0.1%
0.01256516318 1
 
0.1%
0.01258956156 1
 
0.1%
0.0126789572 2
 
0.2%
0.01317512256 2
 
0.2%
0.01338651008 1
 
0.1%
0.01356549656 1
 
0.1%
ValueCountFrequency (%)
1 3
0.3%
0.5133418123 4
0.4%
0.5121218935 2
0.2%
0.483128426 2
0.2%
0.4440992237 4
0.4%
0.432884169 1
 
0.1%
0.4128205068 1
 
0.1%
0.4125033279 3
0.3%
0.3217983671 2
0.2%
0.2995388512 3
0.3%

Age_Standard
Real number (ℝ)

High correlation  Zeros 

Distinct89
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2229381 × 10-16
Minimum-2.2531555
Maximum3.8708717
Zeros177
Zeros (%)19.9%
Negative384
Negative (%)43.1%
Memory size7.1 KiB
2025-08-21T11:08:31.667190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2.2531555
5-th percentile-1.8237502
Q1-0.5924806
median0
Q30.40792596
95-th percentile1.8700586
Maximum3.8708717
Range6.1240272
Interquartile range (IQR)1.0004066

Descriptive statistics

Standard deviation1.0005616
Coefficient of variation (CV)4.5010773 × 1015
Kurtosis0.9662793
Mean2.2229381 × 10-16
Median Absolute Deviation (MAD)0.48488031
Skewness0.43448809
Sum1.9140245 × 10-13
Variance1.0011236
MonotonicityNot monotonic
2025-08-21T11:08:31.719521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 177
 
19.9%
-0.4385718983 30
 
3.4%
-0.5924805998 27
 
3.0%
-0.9002980028 26
 
2.9%
-0.1307544954 25
 
2.8%
0.02315420612 25
 
2.8%
-0.823343652 25
 
2.8%
-0.6694349505 24
 
2.7%
-0.3616175476 23
 
2.6%
0.4848803106 22
 
2.5%
Other values (79) 487
54.7%
ValueCountFrequency (%)
-2.253155489 1
 
0.1%
-2.233916901 1
 
0.1%
-2.227760553 2
 
0.2%
-2.221604205 2
 
0.2%
-2.214678313 1
 
0.1%
-2.208521965 7
0.8%
-2.131567615 10
1.1%
-2.054613264 6
0.7%
-1.977658913 10
1.1%
-1.900704562 4
 
0.4%
ValueCountFrequency (%)
3.870871743 1
 
0.1%
3.409145639 1
 
0.1%
3.178282586 2
0.2%
3.139805411 1
 
0.1%
3.101328236 2
0.2%
2.793510833 1
 
0.1%
2.716556482 3
0.3%
2.639602131 2
0.2%
2.562647781 2
0.2%
2.48569343 4
0.4%

Age_Group
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Adult
688 
Teen
70 
Child
69 
Senior
 
64

Length

Max length6
Median length5
Mean length4.993266
Min length4

Characters and Unicode

Total characters4449
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdult
2nd rowAdult
3rd rowAdult
4th rowAdult
5th rowAdult

Common Values

ValueCountFrequency (%)
Adult 688
77.2%
Teen 70
 
7.9%
Child 69
 
7.7%
Senior 64
 
7.2%

Length

2025-08-21T11:08:31.773358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-21T11:08:31.819028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
adult 688
77.2%
teen 70
 
7.9%
child 69
 
7.7%
senior 64
 
7.2%

Most occurring characters

ValueCountFrequency (%)
d 757
17.0%
l 757
17.0%
A 688
15.5%
u 688
15.5%
t 688
15.5%
e 204
 
4.6%
n 134
 
3.0%
i 133
 
3.0%
T 70
 
1.6%
C 69
 
1.6%
Other values (4) 261
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 757
17.0%
l 757
17.0%
A 688
15.5%
u 688
15.5%
t 688
15.5%
e 204
 
4.6%
n 134
 
3.0%
i 133
 
3.0%
T 70
 
1.6%
C 69
 
1.6%
Other values (4) 261
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 757
17.0%
l 757
17.0%
A 688
15.5%
u 688
15.5%
t 688
15.5%
e 204
 
4.6%
n 134
 
3.0%
i 133
 
3.0%
T 70
 
1.6%
C 69
 
1.6%
Other values (4) 261
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 757
17.0%
l 757
17.0%
A 688
15.5%
u 688
15.5%
t 688
15.5%
e 204
 
4.6%
n 134
 
3.0%
i 133
 
3.0%
T 70
 
1.6%
C 69
 
1.6%
Other values (4) 261
 
5.9%

Interactions

2025-08-21T11:08:29.184733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.242161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.645992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.017276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.303333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.578603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.849950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:29.222773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.298489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.684618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.055951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.340307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.614977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.886936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:29.265825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.406151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.727885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.100632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.382339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.656346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.929231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:29.308221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.471965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.771615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.142793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.423803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.698009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.971590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:29.347055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.530356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.836185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.182949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.462121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.736275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:29.068261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:29.386255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.568146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.880651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.222121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.500373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.773279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:29.107014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:29.425595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.606331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:27.976017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.262615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.538563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:28.810458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-21T11:08:29.145047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-08-21T11:08:31.853483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AgeAge_BinMeanAge_GroupAge_StandardEmbarkedFareFare_MinMaxParchPassengerIdPclassSexSibSpSurvived
Age1.0000.9970.8231.0000.1390.1190.119-0.2170.0420.2650.106-0.1470.158
Age_BinMean0.9971.0000.7300.9970.0900.0950.0950.2460.0000.2630.1130.2400.135
Age_Group0.8230.7301.0000.8230.0310.0690.0690.3000.0000.1900.1160.2900.108
Age_Standard1.0000.9970.8231.0000.1390.1190.119-0.2170.0420.2650.106-0.1470.158
Embarked0.1390.0900.0310.1391.0000.1700.1700.0000.0000.2640.1250.0610.173
Fare0.1190.0950.0690.1190.1701.0001.0000.410-0.0140.4790.1890.4470.283
Fare_MinMax0.1190.0950.0690.1190.1701.0001.0000.410-0.0140.4790.1890.4470.283
Parch-0.2170.2460.300-0.2170.0000.4100.4101.0000.0010.0220.2470.4500.157
PassengerId0.0420.0000.0000.0420.000-0.014-0.0140.0011.0000.0320.066-0.0610.104
Pclass0.2650.2630.1900.2650.2640.4790.4790.0220.0321.0000.1300.1480.337
Sex0.1060.1130.1160.1060.1250.1890.1890.2470.0660.1301.0000.2060.540
SibSp-0.1470.2400.290-0.1470.0610.4470.4470.450-0.0610.1480.2061.0000.187
Survived0.1580.1350.1080.1580.1730.2830.2830.1570.1040.3370.5400.1871.000

Missing values

2025-08-21T11:08:29.486162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-21T11:08:29.573522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAge_BinMeanFare_MinMaxAge_StandardAge_Group
0103Braund, Mr. Owen Harris122.00000010A/5 211717.2500Unknown226.2566800.014151-0.592481Adult
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)038.00000010PC 1759971.2833C85039.2393620.1391360.638789Adult
2313Heikkinen, Miss. Laina026.00000000STON/O2. 31012827.9250Unknown226.2566800.015469-0.284663Adult
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)035.0000001011380353.1000C123239.2393620.1036440.407926Adult
4503Allen, Mr. William Henry135.000000003734508.0500Unknown239.2393620.0157130.407926Adult
5603Moran, Mr. James129.699118003308778.4583Unknown126.2566800.0165100.000000Adult
6701McCarthy, Mr. Timothy J154.000000001746351.8625E46254.8478260.1012291.870059Senior
7803Palsson, Master. Gosta Leonard12.0000003134990921.0750Unknown28.0067000.041136-2.131568Child
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)027.0000000234774211.1333Unknown226.2566800.021731-0.207709Adult
91012Nasser, Mrs. Nicholas (Adele Achem)014.0000001023773630.0708Unknown08.0067000.058694-1.208115Teen
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAge_BinMeanFare_MinMaxAge_StandardAge_Group
88188203Markun, Mr. Johann133.000000003492577.8958Unknown239.2393620.0154120.254017Adult
88288303Dahlberg, Miss. Gerda Ulrika022.00000000755210.5167Unknown226.2566800.020527-0.592481Adult
88388402Banfield, Mr. Frederick James128.00000000C.A./SOTON 3406810.5000Unknown226.2566800.020495-0.130754Adult
88488503Sutehall, Mr. Henry Jr125.00000000SOTON/OQ 3920767.0500Unknown226.2566800.013761-0.361618Adult
88588603Rice, Mrs. William (Margaret Norton)039.0000000538265229.1250Unknown139.2393620.0568480.715743Adult
88688702Montvila, Rev. Juozas127.0000000021153613.0000Unknown226.2566800.025374-0.207709Adult
88788811Graham, Miss. Margaret Edith019.0000000011205330.0000B42226.2566800.058556-0.823344Adult
88888903Johnston, Miss. Catherine Helen "Carrie"029.69911812W./C. 660723.4500Unknown226.2566800.0457710.000000Adult
88989011Behr, Mr. Karl Howell126.0000000011136930.0000C148026.2566800.058556-0.284663Adult
89089103Dooley, Mr. Patrick132.000000003703767.7500Unknown126.2566800.0151270.177063Adult